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Mendeley readers
Attention Score in Context
| Title |
Models of disease behavior in idiopathic pulmonary fibrosis
|
|---|---|
| Published in |
BMC Medicine, September 2015
|
| DOI | 10.1186/s12916-015-0403-7 |
| Pubmed ID | |
| Authors |
Kerri A. Johannson, Brett Ley, Harold R. Collard |
| Abstract |
Idiopathic pulmonary fibrosis is a diffuse parenchymal lung disease of unknown cause. The natural history of disease can vary considerably, making it difficult to predict the clinical trajectory for an individual patient. Accurate prognostication is desirable for clinical management as well as for cohort enrichment in clinical trials of therapeutics. Clinical and biomarker models of disease behavior have been developed to improve prognostication in idiopathic pulmonary fibrosis, with moderate predictive capabilities. Integrated prediction models that combine both clinical and biomarker variables will improve prognostication for patients and improved cohort enrichment strategies for clinical trials. This goal may be best achieved through collaborative patient registries with prospectively collected biological samples that allow for characterization of disease behavior in idiopathic pulmonary fibrosis. |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 1 | 3% |
| Unknown | 36 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 7 | 19% |
| Researcher | 5 | 14% |
| Professor | 3 | 8% |
| Student > Postgraduate | 3 | 8% |
| Student > Bachelor | 2 | 5% |
| Other | 6 | 16% |
| Unknown | 11 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 54% |
| Unspecified | 1 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Computer Science | 1 | 3% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 2 | 5% |
| Unknown | 11 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 September 2015.
All research outputs
#5,748,419
of 22,829,083 outputs
Outputs from BMC Medicine
#2,275
of 3,430 outputs
Outputs of similar age
#69,405
of 274,665 outputs
Outputs of similar age from BMC Medicine
#74
of 94 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 3,430 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 274,665 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 94 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.